Arranging information describing items within a page maintained in an online system based on an interaction with a link to the page

ABSTRACT

An online system receives information describing (an) item(s) associated with an entity and a content item including an image. The online system accesses and applies a trained machine-learning model to predict a probability that the content item includes an image of an item associated with the entity. If the probability is at least a threshold probability, a link to a page associated with the item that includes a set of the information describing the item(s) is added to the content item by the online system. Responsive to receiving an interaction with the link from a user presented with the content item, the online system determines a measure of similarity between the item and each additional item based on the information describing the item(s), arranges the set of the information describing the item(s) within the page based on the measure(s) of similarity, and sends the page for display to the user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 16/864,729, filed May 1, 2020, which is incorporated by reference inits entirety.

TECHNICAL FIELD

This disclosure relates generally to online systems, and morespecifically to arranging information describing items within a pagemaintained in an online system based on an interaction with a link tothe page.

BACKGROUND

An online system allows its users to connect and communicate with otheronline system users. Users create profiles in the online system that aretied to their identities and include information about the users, suchas interests and demographic information. The users may be individualsor entities such as corporations or charities. Because of the popularityof online systems and the significant amount of user-specificinformation maintained in online systems, an online system provides anideal forum for allowing users to share content by creating contentitems for presentation to additional online system users. For example,users may share photos or videos they have uploaded by creating contentitems that include the photos or videos that are presented to additionalusers to whom they are connected in the online system.

To increase awareness among online system users about items (e.g.,products) associated with entities (e.g., merchants) having a presenceon an online system, content items including images of the items thatare presented to the users may include links to pages maintained in theonline system that include additional information about the items andallow the users to perform various actions associated with the items.For example, a content item that includes an image of a shirt that amerchant offers for sale also may include a link to a page maintained inan online system that includes additional information about the shirt(e.g., additional images of the shirt, details about colors and sizes ofthe shirt that are available, etc.). In this example, online systemusers who are presented with the content item and who interact with thelink are directed to the page at which the users may view the additionalinformation about the shirt, purchase the shirt from the merchant,and/or share the page with additional online system users. In the aboveexample, the page also may include similar information about additionalproducts that the merchant offers for sale that the users directed tothe page also may purchase from the merchant.

However, online system users who are directed to a page maintained in anonline system in response to interacting with a link included in acontent item may not become aware of items for which the users arelikely to have affinities if information describing these items is noteasily accessible to the users. In the above example, suppose that theshirt is a blue dress shirt for men and that information describingother types of clothing and shoes is also presented in the page to whicha user is directed upon interacting with the link. In this example, evenif additional shirts of a similar style for which the user is likely tohave an affinity may be purchased from the merchant via the page, theuser may not become aware of them if the user is required to scrolland/or sort through information describing several other items beforebeing able to view information describing these shirts. Although anentity associated with items described in a page maintained in an onlinesystem may specify which items are related to each other so that similaritems may be grouped together within the page, this may be atime-consuming and resource-intensive process, especially if the entityis associated with several items. Furthermore, the entity may berequired to update this information each time new items are added to orremoved from the page.

SUMMARY

To increase awareness among online system users about items associatedwith entities having a presence on an online system, content itemsincluding images of the items that are presented to the users mayinclude links to pages maintained in the online system that includeadditional information about the items and allow the users to performvarious actions associated with the items. However, the users may notbecome aware of items for which they are likely to have affinities ifinformation describing these items is not easily accessible to the userswithin a page maintained in the online system to which the users aredirected in response to interacting with a link included in a contentitem. Although information describing similar items may be groupedtogether within the page if an entity associated with the itemsspecifies which items are related to each other, this may be atime-consuming and resource-intensive process, especially if the entityis associated with several items. Furthermore, this information may berequired to be updated as new items are added to or removed from thepage.

To address this issue, an online system arranges information describingitems within a page maintained in the online system based on aninteraction with a link to the page. More specifically, the onlinesystem receives information describing one or more items associated withan entity having a presence on the online system, in which theinformation includes one or more images of each item. The online systemalso receives a content item to be presented to one or more viewingusers of the online system, in which the content item includes an image.The online system accesses a trained machine-learning model that istrained based on the information describing the item(s). The onlinesystem then applies the trained machine-learning model to a set ofattributes of the content item to predict a probability that the contentitem includes an image of a “subject item” associated with the entityand determines whether the predicted probability is at least a thresholdprobability. In response to determining that the predicted probabilityis at least the threshold probability, the online system adds a link tothe content item. The link added to the content item corresponds to apage associated with the subject item that is maintained in the onlinesystem and includes a set of the information describing the item(s).Upon determining an opportunity to present content to a viewing user ofthe online system, the online system sends the content item includingthe link for display to the viewing user. In response to receiving aninteraction with the link from the viewing user, the online systemdetermines a measure of similarity between the subject item and eachadditional item included among the one or more items based on theinformation describing the item(s). The online system then arranges theset of the information describing the item(s) within the page based onthe measure of similarity between the subject item and each additionalitem and sends the page for display to the viewing user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system environment in which an onlinesystem operates, in accordance with an embodiment.

FIG. 2 is a block diagram of an online system, in accordance with anembodiment.

FIG. 3 is a flow chart of a method for arranging information describingitems within a page maintained in an online system based on aninteraction with a link to the page, in accordance with an embodiment.

FIG. 4 is an example of an embedding space that includes embeddingscorresponding to items that are associated with an entity having apresence on an online system, in accordance with an embodiment.

FIG. 5 is an example of a hierarchy of categories associated with itemsthat are associated with an entity having a presence on an onlinesystem, in accordance with an embodiment.

FIG. 6 illustrates an example of arranging a set of informationdescribing items that are associated with an entity having a presence onan online system within a page maintained in the online system based onan interaction with a link to the page, in accordance with anembodiment.

The figures depict various embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the structures and methodsillustrated herein may be employed without departing from the principlesdescribed herein.

DETAILED DESCRIPTION System Architecture

FIG. 1 is a block diagram of a system environment 100 for an onlinesystem 140. The system environment 100 shown by FIG. 1 comprises one ormore client devices 110, a network 120, one or more third-party systems130, and the online system 140. In alternative configurations, differentand/or additional components may be included in the system environment100.

The client devices 110 are one or more computing devices capable ofreceiving user input as well as transmitting and/or receiving data viathe network 120. In one embodiment, a client device 110 is aconventional computer system, such as a desktop or a laptop computer.Alternatively, a client device 110 may be a device having computerfunctionality, such as a personal digital assistant (PDA), a mobiletelephone, a smartphone or another suitable device. A client device 110is configured to communicate via the network 120. In one embodiment, aclient device 110 executes an application allowing a user of the clientdevice 110 to interact with the online system 140. For example, a clientdevice 110 executes a browser application to enable interaction betweenthe client device 110 and the online system 140 via the network 120. Inanother embodiment, a client device 110 interacts with the online system140 through an application programming interface (API) running on anative operating system of the client device 110, such as IOS® orANDROID™.

The client devices 110 are configured to communicate via the network120, which may comprise any combination of local area and/or wide areanetworks, using both wired and/or wireless communication systems. In oneembodiment, the network 120 uses standard communications technologiesand/or protocols. For example, the network 120 includes communicationlinks using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 3G, 4G, code divisionmultiple access (CDMA), digital subscriber line (DSL), etc. Examples ofnetworking protocols used for communicating via the network 120 includemultiprotocol label switching (MPLS), transmission controlprotocol/Internet protocol (TCP/IP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP), and file transfer protocol(FTP). Data exchanged over the network 120 may be represented using anysuitable format, such as hypertext markup language (HTML) or extensiblemarkup language (XML). In some embodiments, all or some of thecommunication links of the network 120 may be encrypted using anysuitable technique or techniques.

One or more third-party systems 130 may be coupled to the network 120for communicating with the online system 140, which is further describedbelow in conjunction with FIG. 2. In one embodiment, a third-partysystem 130 is an application provider communicating informationdescribing applications for execution by a client device 110 orcommunicating data to client devices 110 for use by an applicationexecuting on the client device 110. In other embodiments, a third-partysystem 130 (e.g., a content publisher) provides content or otherinformation for presentation via a client device 110. A third-partysystem 130 also may communicate information to the online system 140,such as advertisements, content, or information about an applicationprovided by the third-party system 130.

FIG. 2 is a block diagram of an architecture of the online system 140.The online system 140 shown in FIG. 2 includes a user profile store 205,a content store 210, an action logger 215, an action log 220, an edgestore 225, a machine-learning module 230, a prediction module 235, auser interface generator 240, a content selection module 245, arelationship determination module 250, and a web server 255. In otherembodiments, the online system 140 may include additional, fewer, ordifferent components for various applications. Conventional componentssuch as network interfaces, security functions, load balancers, failoverservers, management and network operations consoles, and the like arenot shown so as to not obscure the details of the system architecture.

Each user of the online system 140 is associated with a user profile,which is stored in the user profile store 205. A user profile includesdeclarative information about the user that was explicitly shared by theuser and also may include profile information inferred by the onlinesystem 140. In one embodiment, a user profile includes multiple datafields, each describing one or more attributes of the correspondingonline system user. Examples of information stored in a user profileinclude biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, gender,hobbies or preferences, locations and the like. A user profile also maystore other information provided by the user, for example, images orvideos. In certain embodiments, images of users may be tagged withinformation identifying the online system users displayed in an image,with information identifying the images in which a user is tagged storedin the user profile of the user. A user profile in the user profilestore 205 also may maintain references to actions by the correspondinguser performed on content items in the content store 210 and stored inthe action log 220.

While user profiles in the user profile store 205 frequently areassociated with individuals, allowing individuals to interact with eachother via the online system 140, user profiles also may be stored forentities such as businesses or organizations. This allows an entity toestablish a presence on the online system 140 for connecting andexchanging content with other online system users. The entity may postinformation about itself, about its products or provide otherinformation to users of the online system 140 using a brand pageassociated with the entity's user profile. Other users of the onlinesystem 140 may connect to the brand page to receive information postedto the brand page or to receive information from the brand page. A userprofile associated with the brand page may include information about theentity itself, providing users with background or informational dataabout the entity.

The content store 210 stores objects that each represent various typesof content. Examples of content represented by an object include a pagepost, a status update, a photograph, a video, a link, a shared contentitem, a gaming application achievement, a check-in event at a localbusiness, a page (e.g., a brand page), an advertisement, or any othertype of content. Online system users may create objects stored by thecontent store 210, such as status updates, photos tagged by users to beassociated with other objects in the online system 140, events, groupsor applications. In some embodiments, objects are received fromthird-party applications or third-party applications separate from theonline system 140. In one embodiment, objects in the content store 210represent single pieces of content, or content “items.” Hence, onlinesystem users are encouraged to communicate with each other by postingtext and content items of various types of media to the online system140 through various communication channels. This increases the amount ofinteraction of users with each other and increases the frequency withwhich users interact within the online system 140.

The content store 210 also may store information describing one or moreitems associated with one or more entities having a presence on theonline system 140. In some embodiments, the items may include products,such as clothing items, shoes, electronics, cars, furniture, etc.,digital items, such as music, movies, games, applications, etc., or anyother suitable types of items. In various embodiments, entitiesassociated with the items may include merchants of the items,manufacturers/creators of the items, distributors of the items, or anyother suitable types of entities that may be associated with the items.Information stored in the content store 210 describing an item mayinclude one or more images of the item, a brand or an artist associatedwith the item, a type of the item, a style or a genre associated withthe item, a version or a model associated with the item, a colorassociated with the item, a material or a set of specificationsassociated with the item, a price associated with the item, or any othersuitable types of information that may be associated with the item. Forexample, if an item corresponds to a specific pair of running shoes,information describing the running shoes stored in the content store 210may include one or more images of the shoes, a brand of the shoes,information indicating that the item is a type of shoe, informationindicating that the style of the shoes corresponds to running shoes,information describing one or more colors of the shoes, informationdescribing one or more materials of the shoes (e.g., leather, cloth,rubber, etc.), and a retail price of the shoes. Information describingan item may be received from an entity having a presence on the onlinesystem 140 associated with the item. In the above example, informationdescribing the running shoes may be received from an entitycorresponding to a manufacturer of the running shoes.

In some embodiments, information stored in the content store 210describing items associated with entities having a presence on theonline system 140 may be represented by embeddings generated by therelationship determination module 250, as described below. In suchembodiments, each embedding in an embedding space associated with anentity having a presence on the online system 140 may correspond to anitem associated with the entity, in which embeddings that are closertogether represent items that are more closely related than embeddingsthat are further apart. For example, suppose that a first embedding inan embedding space corresponds to a white cotton turtleneck sweaterassociated with an entity and that a second embedding in the embeddingspace corresponds to a white cotton crewneck sweater associated with theentity. In this example, since the first embedding and the secondembedding correspond to items of clothing of the same type (i.e.,sweaters), material, and color, the embeddings may be closer together inthe embedding space than they are to embeddings corresponding to othertypes of clothing items associated with the entity (e.g., pants,outerwear, etc.) of different materials and colors. In the aboveexample, embeddings corresponding to types of clothing items associatedwith the entity also may be closer together in the embedding space thanthey are to embeddings corresponding to other types of non-clothingitems associated with the entity (e.g., shoes, accessories, etc.).

In some embodiments, the content store 210 may store informationdescribing one or more categories associated with items that areassociated with entities having a presence on the online system 140. Acategory associated with an item may be determined by the relationshipdetermination module 250 based on information describing the item, asdescribed below. For example, suppose that an item corresponds to a pairof $99.99 white leather sandals by Brand A. In this example, informationstored in the content store 210 may include information describing oneor more categories associated with the item, such as a brand of the item(i.e., Brand A), a type of the item (i.e., shoes), a style of the item(i.e., sandals), a color of the item (i.e., white), a material of theitem (i.e., leather), and/or a price of the item (i.e., $99.99).Information describing a category stored in the content store 210 alsomay include more specific categories (i.e., subcategories). In the aboveexample, the category of shoes may include additional categories thatare more specific than the category of shoes (e.g., styles of shoes,colors of shoes, materials of shoes, prices of shoes, etc.). Inembodiments in which the content store 210 stores information describingan item, information describing one or more categories associated withthe item may be stored in association with the information describingthe item.

In some embodiments, information stored in the content store 210describing one or more categories associated with items that areassociated with entities having a presence on the online system 140 maybe arranged in one or more hierarchies of categories generated by therelationship determination module 250, as described below. In suchembodiments, a hierarchy of categories includes multiple nodesrepresenting the categories and edges connecting the nodes representrelationships between the categories, in which items associated withcategories corresponding to nodes that are closer together are moreclosely related than items associated with categories corresponding tonodes that are further apart. Furthermore, different levels of ahierarchy represent different levels of specificity, such thatcategories represented by nodes at a lowest level of the hierarchycorrespond to a most specific level of specificity and categoriesrepresented by nodes at a highest level of the hierarchy correspond to amost general level of specificity. For example, suppose that in ahierarchy of categories, a node at a highest level of the hierarchyrepresents the category of a brand. In this example, each additionalnode connected by an edge to a node at a higher level of the hierarchymay represent a more specific category (e.g., a type of an item, a styleof an item, a color of an item, a material of an item, a price of anitem, etc.) within a category corresponding to the node at the higherlevel to which it is connected. The content store 210 is furtherdescribed below in conjunction with FIG. 3.

The action logger 215 receives communications about user actionsinternal to and/or external to the online system 140, populating theaction log 220 with information about user actions. Examples of actionsinclude adding a connection to another user, sending a message toanother user, uploading an image, reading a message from another user,viewing content associated with another user, and attending an eventposted by another user. In addition, a number of actions may involve anobject and one or more particular users, so these actions are associatedwith those users as well and stored in the action log 220.

The action log 220 may be used by the online system 140 to track useractions in the online system 140, as well as actions in third-partysystems 130 that communicate information to the online system 140. Usersmay interact with various objects in the online system 140, andinformation describing these interactions is stored in the action log220. Examples of interactions with objects include: commenting on posts,sharing links, checking-in to physical locations via a client device110, accessing content items, and any other suitable interactions.Additional examples of interactions with objects in the online system140 that are included in the action log 220 include: commenting on aphoto album, communicating with a user, establishing a connection withan object, joining an event, joining a group, creating an event,authorizing an application, using an application, expressing apreference for an object (“liking” the object), and engaging in atransaction. Additionally, the action log 220 may record a user'sinteractions with advertisements in the online system 140 as well aswith other applications operating in the online system 140. In someembodiments, data from the action log 220 is used to infer interests orpreferences of a user, augmenting the interests included in the user'suser profile and allowing a more complete understanding of userpreferences.

The action log 220 also may store user actions taken on a third-partysystem 130, such as an external website, and communicated to the onlinesystem 140. For example, an e-commerce website may recognize a user ofan online system 140 through a social plug-in enabling the e-commercewebsite to identify the user of the online system 140. Because users ofthe online system 140 are uniquely identifiable, e-commerce websites,such as in the preceding example, may communicate information about auser's actions outside of the online system 140 to the online system 140for association with the user. Hence, the action log 220 may recordinformation about actions users perform on a third-party system 130,including webpage viewing histories, advertisements that were engaged,purchases made, and other patterns from shopping and buying.Additionally, actions a user performs via an application associated witha third-party system 130 and executing on a client device 110 may becommunicated to the action logger 215 for storing in the action log 220by the application for recordation and association with the user by theonline system 140.

In one embodiment, the edge store 225 stores information describingconnections between users and other objects in the online system 140 asedges. Some edges may be defined by users, allowing users to specifytheir relationships with other users. For example, users may generateedges with other users that parallel the users' real-life relationships,such as friends, co-workers, partners, and so forth. Other edges aregenerated when users interact with objects in the online system 140,such as expressing interest in a page in the online system 140, sharinga link with other users of the online system 140, and commenting onposts made by other users of the online system 140.

In one embodiment, an edge may include various features eachrepresenting characteristics of interactions between users, interactionsbetween users and objects, or interactions between objects. For example,features included in an edge describe the rate of interaction betweentwo users, how recently two users have interacted with each other, therate or amount of information retrieved by one user about an object, orthe number and types of comments posted by a user about an object. Thefeatures also may represent information describing a particular objector user. For example, a feature may represent the level of interest thata user has in a particular topic, the rate at which the user logs intothe online system 140, or information describing demographic informationabout the user. Each feature may be associated with a source object oruser, a target object or user, and a feature value. A feature may bespecified as an expression based on values describing the source objector user, the target object or user, or interactions between the sourceobject or user and target object or user; hence, an edge may berepresented as one or more feature expressions.

The edge store 225 also stores information about edges, such as affinityscores for objects, interests, and other users. Affinity scores, or“affinities,” may be computed by the online system 140 over time toapproximate a user's interest in an object or in another user in theonline system 140 based on the actions performed by the user. A user'saffinity may be computed by the online system 140 over time toapproximate the user's interest in an object, a topic, or another userin the online system 140 based on actions performed by the user.Computation of affinity is further described in U.S. patent applicationSer. No. 12/978,265, filed on Dec. 23, 2010, U.S. patent applicationSer. No. 13/690,254, filed on Nov. 30, 2012, U.S. patent applicationSer. No. 13/689,969, filed on Nov. 30, 2012, and U.S. patent applicationSer. No. 13/690,088, filed on Nov. 30, 2012, each of which is herebyincorporated by reference in its entirety. Multiple interactions betweena user and a specific object may be stored as a single edge in the edgestore 225, in one embodiment. Alternatively, each interaction between auser and a specific object is stored as a separate edge. In someembodiments, connections between users may be stored in the user profilestore 205, or the user profile store 205 may access the edge store 225to determine connections between users.

The machine-learning module 230 may train a machine-learning model topredict a probability that a content item includes an image of an itemassociated with an entity having a presence on the online system 140. Asdescribed above, examples of items include products, such as clothingitems, shoes, electronics, cars, furniture, etc., digital items, such asmusic, movies, games, applications, etc., while entities associated withthe items may include merchants of the items, manufacturers/creators ofthe items, distributors of the items, etc. The machine-learning modelmay be a convolutional neural network, a deep learning model, or anyother suitable machine-learning model. In some embodiments, themachine-learning module 230 may train multiple machine-learning modelsthat collectively predict a probability that a content item includes animage of an item associated with an entity having a presence on theonline system 140.

In some embodiments, the machine-learning module 230 may train themachine-learning model based on a set of training data received from oneor more entities having a presence on the online system 140. In suchembodiments, the set of training data may include information describingone or more items associated with each entity, as well as informationdescribing one or more items that are not associated with an entityhaving a presence on the online system 140. Information describing eachitem included among the set of training data may include one or moreimages of the item, a brand or an artist associated with the item, atype of the item, a style or a genre associated with the item, a versionor a model associated with the item, a color associated with the item, amaterial or a set of specifications associated with the item, a priceassociated with the item, or any other suitable types of informationthat may be associated with the item. In embodiments in which the set oftraining data includes an image of an item, the machine-learning modelmay be trained based on a set of pixel values associated with the image(e.g., a set of pixel values describing a size of the image, aresolution of the image, a brightness of one or more pixels within theimage, red, green and blue color component intensities of one or morepixels within the image, etc.).

In some embodiments, the machine-learning module 230 also may train themachine-learning model based on additional types of information. Invarious embodiments, the machine-learning module 230 also may train themachine-learning model based on a set of attributes of each of atraining set of content items, as well as information indicating whethereach of the training set of content items includes an imagecorresponding to an item associated with an entity having a presence onthe online system 140. Examples of attributes of a content item includea set of pixel values associated with an image included in the contentitem, a tag included in the content item, a caption included in thecontent item, a location associated with the content item, a comment onthe content item, an audience associated with the content item, aninferred relationship between the content item and a topic, etc.

In some embodiments, once trained, the machine-learning model may firstdetect one or more objects within an image included in a content item(e.g., an image included in one or more frames of a video included inthe content item). The machine-learning model may do so by applying oneor more object detection methods to the image. The machine-learningmodel also may identify locations of objects detected within an image(e.g., by generating a bounding box surrounding each object). In someembodiments, the machine-learning model uses one or more objectdetection methods to detect objects within an image and to generatebounding boxes corresponding to each of the detected objects. Whendetecting objects included in an image, the machine-learning model alsomay identify a category or a type (e.g., a category or a type associatedwith a product or other item) corresponding to each object detectedwithin the image based on attributes of the object. For example, anobject detection method applied by the machine-learning model associatesdifferent categories or types with objects based on attributes of theobjects and the machine-learning model associates a category or a typefrom the object detection method with a detected object. In thisexample, if an object detected within an image corresponds to a sportscar of a specific make and model, based on attributes of the object(e.g., headlights, a windshield, four wheels, one or more side-viewmirrors, etc.), an object detection method applied by themachine-learning model may associate the object with the category/typecorresponding to cars.

In embodiments in which the machine-learning model identifies a categoryor a type associated with each object detected within an image includedin a content item, the machine-learning module 230 may train themachine-learning model based on a training set of images includingimages of items associated with different categories or types. In someembodiments, the training set of images may include publicly availableinformation identifying different categories or types associated withimages of various items. The machine-learning model also may be trainedbased on attributes that characterize each of the training set ofimages, as well as information indicating a category or a typeassociated with an item corresponding to each of the training set ofimages. Examples of attributes that characterize an image includeshapes, edges, curves, textures, etc. detected within the image,components of various categories or types of items (e.g., surfaces,handles, wheels, fasteners, etc.), or any other suitable attributes thatmay characterize an image.

In some embodiments, once trained, the machine-learning model also maymake one or more predictions that each correspond to a probability thatan object detected within an image included in a content itemcorresponds to a specific item associated with an entity having apresence on the online system 140. The machine-learning model may makeeach prediction by comparing each object detected within an image toimages of items (e.g., images of products included in a product catalog)associated with one or more entities having a presence on the onlinesystem 140. The machine-learning model then outputs one or moreprobabilities that each object detected within an image included in acontent item matches different items associated with one or moreentities having a presence on the online system 140.

In embodiments in which the machine-learning model predicts aprobability that an object detected within an image corresponds to aspecific item associated with an entity having a presence on the onlinesystem 140, the machine-learning module 230 may train themachine-learning model based on comparisons of objects detected withinimages to images of items associated with one or more entities having apresence on the online system 140. In some embodiments, themachine-learning module 230 trains the machine-learning model to predicta probability that an object detected within an image matches an itemassociated with an entity having a presence on the online system 140based on prior matching of objects detected within images to differentitems associated with entities having a presence on the online system140. For example, the machine-learning module 230 applies a label to anobject detected within an image indicating that the object matches anitem associated with an entity based on attributes of the object (e.g.,logos, trademarks, emblems, icons, patterns, prints, etc.). From thelabeled attributes of objects extracted from images, themachine-learning module 230 trains the machine-learning model using anysuitable training method or combination of training methods (e.g., backpropagation if the machine-learning model is a neural network, curvefitting techniques if the machine-learning model is a linear regressionmodel, etc.). The functionality of the machine-learning module 230 isfurther described below in conjunction with FIG. 3.

The prediction module 235 accesses (e.g., as shown in step 315 of FIG.3) a trained machine-learning model and applies (e.g., as shown in step320 of FIG. 3) the machine-learning model to a set of attributes of acontent item to predict a probability that the content item includes animage of an item associated with an entity having a presence on theonline system 140. In some embodiments, the machine-learning model maybe trained by the machine-learning module 230, while in otherembodiments, the machine-learning model may be trained by a third-partysystem 130. To apply the machine-learning model, the prediction module235 provides an input to the machine-learning model that includes a setof attributes of a content item. Examples of attributes of the contentitem that may be included in the input include a set of pixel valuesassociated with an image included in the content item, a tag included inthe content item, a link included in the content item, a captionincluded in the content item, a location associated with the contentitem, a comment on the content item, an audience associated with thecontent item, an inferred relationship between the content item and atopic, etc. Based on the set of attributes, the machine-learning modelpredicts a probability that the content item includes an image of anitem associated with an entity having a presence on the online system140. The prediction module 235 then receives an output from themachine-learning model corresponding to the predicted probability. Insome embodiments, the prediction module 235 may access and applymultiple machine-learning models that collectively predict a probabilitythat a content item includes an image of an item associated with anentity having a presence on the online system 140 in an analogousmanner.

The prediction module 235 also determines (e.g., as shown in step 325 ofFIG. 3) whether a probability that a content item includes an image ofan item associated with an entity having a presence on the online system140 predicted by the machine-learning model is at least a thresholdprobability. In some embodiments, the prediction module 235 may do so bycomparing the predicted probability to the threshold probability. Forexample, suppose that an output received by the prediction module 235from the machine-learning model corresponds to an 85.1% probability thata content item includes an image of an item associated with an entityhaving a presence on the online system 140. In this example, if thethreshold probability corresponds to an 85% probability, the predictionmodule 235 determines that the predicted probability is at least thethreshold probability since 85.1% is equal to or greater than 85%.Alternatively, in the above example, if the output received by theprediction module 235 from the machine-learning model corresponds to an84% probability, the prediction module 235 determines that the predictedprobability is less than the threshold probability since 84% is notequal to or greater than 85%. The functionality of the prediction module235 is further described below in conjunction with FIG. 3.

The user interface generator 240 adds (e.g., as shown in step 330 ofFIG. 3) a link to a content item. In some embodiments, the userinterface generator 240 alternatively may add a button that acts as thelink. The link may correspond to a page maintained in the online system140 and may be associated with an item that is associated with an entityhaving a presence on the online system 140. Furthermore, the page alsomay include information describing one or more items associated with theentity. As described above, information describing an item associatedwith an entity may include one or more images of the item, a brand or anartist associated with the item, a type of the item, a style or a genreassociated with the item, a version or a model associated with the item,a color associated with the item, a material or a set of specificationsassociated with the item, a price associated with the item, etc.

In some embodiments, a page corresponding to a link added to a contentitem by the user interface generator 240 also may include one or moreinteractive elements (e.g., buttons, scroll bars, etc.) that allow aviewing user of the online system 140 to view information describing oneor more items associated with an entity within the page and/or toperform various actions associated with the item(s). For example,suppose that an item associated with a page corresponding to a linkadded to a content item by the user interface generator corresponds to at-shirt associated with an entity having a presence on the online system140 and that information describing the t-shirt included in the pageincludes a brand of the t-shirt, a name or a model of the t-shirt, aprice of the t-shirt, etc. Continuing with this example, a viewing userof the online system 140 presented with the page may view additionalinformation (e.g., materials, care instructions, reviews, etc.) aboutthe t-shirt by clicking on a button (e.g., a button labeled “moredetails”) associated with the t-shirt and may add the t-shirt to a cartby clicking on an additional button (e.g., a button labeled “add tocart”) associated with the t-shirt. In the above example, the viewinguser also may interact with a scroll bar to view information describingadditional items (e.g., additional t-shirts) associated with the entitywithin the page.

The user interface generator 240 also arranges (e.g., as shown in step350 of FIG. 3) information describing one or more items associated withan entity having a presence on the online system 140 within a pagemaintained in the online system 140, in which the page is associatedwith an item that is associated with the entity. The user interfacegenerator 240 may arrange the information based on a measure ofsimilarity between the item associated with the page and each additionalitem associated with the entity, such that items that are more similarto the item associated with the page are in a more prominent positionwithin an area of the page than items that are not as similar to theitem associated with the page. In the above example, since the itemassociated with the page maintained in the online system 140 correspondsto a t-shirt, the user interface generator 240 may arrange informationdescribing additional items associated with the entity within the pagebased on a measure of similarity of each additional item to the t-shirt(e.g., by ranking the additional items based on how similar they are tothe t-shirt). In this example, information describing items associatedwith the entity that are most similar to the t-shirt (e.g., othert-shirts of a similar fit, color, pattern, etc.) may be arranged in moreprominent positions of an area of the page than information describingother items associated with the entity that are not as similar to thet-shirt.

In some embodiments, the user interface generator 240 also oralternatively may include information describing a set of itemsassociated with an entity in a page maintained in the online system 140only if each item has at least a threshold measure of similarity to anitem that is associated with both the entity and the page. For example,if three out of six items associated with an entity having a presence onthe online system 140 have at least a threshold measure of similarity toan item associated with both the entity and a page maintained in theonline system 140, the user interface generator 240 may include only theinformation describing the three items within the page. In the aboveexample, the user interface generator 240 also may arrange theinformation describing the three items based on a measure of similaritybetween the item associated with the page and each of the three itemsassociated with the entity, such that items that are more similar to theitem associated with the page are in more prominent positions within anarea of the page than items that are less similar to the item associatedwith the page. The functionality of the user interface generator 240 isfurther described below in conjunction with FIGS. 3 and 6.

The content selection module 245 may identify one or more candidatecontent items eligible for presentation to a viewing user of the onlinesystem 140. Candidate content items eligible for presentation to theviewing user are retrieved from the content store 210 or from anothersource by the content selection module 245, which may rank the candidatecontent items and select one or more of the candidate content items forpresentation to the viewing user. A candidate content item eligible forpresentation to a viewing user is a content item associated with atleast a threshold number of targeting criteria satisfied bycharacteristics of the viewing user or is a content item that is notassociated with targeting criteria. In various embodiments, the contentselection module 245 includes candidate content items eligible forpresentation to a viewing user in one or more content selectionprocesses, which identify a set of content items for presentation to theviewing user. For example, the content selection module 245 determinesmeasures of relevance of various candidate content items to a viewinguser based on characteristics associated with the viewing user by theonline system 140 and based on the viewing user's affinity for differentcandidate content items. Based on the measures of relevance, the contentselection module 245 selects content items for presentation to theviewing user. As an additional example, the content selection module 245selects content items having the highest measures of relevance or havingat least a threshold measure of relevance for presentation to a viewinguser. Alternatively, the content selection module 245 ranks candidatecontent items based on their associated measures of relevance andselects content items having the highest positions in the ranking orhaving at least a threshold position in the ranking for presentation toa viewing user.

Content items selected for presentation to a viewing user may beassociated with bid amounts. The content selection module 245 may usethe bid amounts associated with candidate content items when selectingcontent for presentation to the viewing user. In various embodiments,the content selection module 245 determines an expected value associatedwith various candidate content items based on their bid amounts andselects content items associated with a maximum expected value orassociated with at least a threshold expected value for presentation toa viewing user. An expected value associated with a candidate contentitem represents an expected amount of compensation to the online system140 for presenting the candidate content item. For example, the expectedvalue associated with a candidate content item is a product of thecandidate content item's bid amount and a likelihood of a viewing userinteracting with content from the candidate content item. The contentselection module 245 may rank candidate content items based on theirassociated bid amounts and select content items having at least athreshold position in the ranking for presentation to a viewing user. Insome embodiments, the content selection module 245 ranks both candidatecontent items not associated with bid amounts and candidate contentitems associated with bid amounts in a unified ranking based on bidamounts and measures of relevance associated with the candidate contentitems. Based on the unified ranking, the content selection module 245selects content for presentation to the viewing user. Selecting contentitems through a unified ranking is further described in U.S. patentapplication Ser. No. 13/545,266, filed on Jul. 10, 2012, which is herebyincorporated by reference in its entirety.

For example, the content selection module 245 receives a request topresent a feed of content to a viewing user of the online system 140.The feed may include one or more advertisements as well as other contentitems, such as stories describing actions associated with other onlinesystem users connected to the viewing user. The content selection module245 accesses one or more of the user profile store 205, the contentstore 210, the action log 220, and the edge store 225 to retrieveinformation about the viewing user. For example, stories or other dataassociated with users connected to the viewing user are retrieved. Theretrieved stories or other content items are analyzed by the contentselection module 245 to identify candidate content that is likely to berelevant to the viewing user. For example, stories associated with usersnot connected to the viewing user or stories associated with users forwhich the viewing user has less than a threshold affinity are discardedas candidate content. Based on various criteria, the content selectionmodule 245 selects one or more of the content items identified ascandidate content for presentation to the viewing user. The selectedcontent items may be included in a feed of content that is presented tothe viewing user. For example, the feed of content includes at least athreshold number of content items describing actions associated withusers connected to the viewing user via the online system 140.

In various embodiments, the content selection module 245 presentscontent to a viewing user through a newsfeed including a plurality ofcontent items selected for presentation to the viewing user. One or moreadvertisements also may be included in the feed. The content selectionmodule 245 may also determine the order in which selected content itemsare presented via the feed. For example, the content selection module245 orders content items in a feed based on likelihoods of a viewinguser interacting with various content items. The functionality of thecontent selection module 245 is further described below in conjunctionwith FIG. 3.

The relationship determination module 250 determines (e.g., as shown instep 345 of FIG. 3) a measure of similarity between an item associatedwith an entity having a presence on the online system 140 and one ormore additional items associated with the entity. The relationshipdetermination module 250 may determine the measure of similarity basedon information describing the items. As described above, informationdescribing an item associated with an entity may include one or moreimages of the item, a brand or an artist associated with the item, atype of the item, a style or a genre associated with the item, a versionor a model associated with the item, a color associated with the item, amaterial or a set of specifications associated with the item, a priceassociated with the item, etc. For example, suppose that an itemassociated with an entity having a presence on the online system 140corresponds to a pair of skis and that information describing the itemindicates that the skis are a type of sporting equipment for wintersports. In this example, suppose also that additional items associatedwith the entity include a painting and a pair of goggles and thatinformation describing the additional items indicate that the paintingis a home decor item while the pair of goggles also are a type ofsporting equipment for winter sports. In the above example, based on theinformation describing each item associated with the entity, therelationship determination module 250 may determine that there is agreater measure of similarity between the skis and the goggles thanbetween the skis and the painting.

In some embodiments, the relationship determination module 250 maydetermine a measure of similarity between items associated with anentity having a presence on the online system 140 based on distancesbetween embeddings in an embedding space associated with the entity, inwhich each embedding corresponds to an item associated with the entity.In such embodiments, the relationship determination module 250 maygenerate an embedding corresponding to an item associated with theentity based on information describing the item (e.g., one or moreimages of the item, a brand or an artist associated with the item, atype of the item, a style or a genre associated with the item, a versionor a model associated with the item, a color associated with the item, amaterial or a set of specifications associated with the item, a priceassociated with the item, etc.). In some embodiments, the relationshipdetermination module 250 also may store an embedding corresponding to anitem that is associated with an entity having a presence on the onlinesystem 140 in the content store 210. The relationship determinationmodule 250 may determine a measure of similarity between two itemsassociated with an entity having a presence on the online system 140 byidentifying embeddings corresponding to each item within an embeddingspace associated with the entity and determining the measure ofsimilarity between the items based on a distance between the embeddings,such that the measure of similarity between the items is inverselyproportional to the distance between the embeddings.

In some embodiments, the relationship determination module 250 maygenerate a hierarchy of categories associated with one or more itemsassociated with an entity having a presence on the online system 140. Insuch embodiments, the relationship determination module 250 may do sobased on information describing each item (e.g., one or more images ofeach item, a brand or an artist associated with each item, a type ofeach item, a style or a genre associated with each item, a version or amodel associated with each item, a color associated with each item, amaterial or a set of specifications associated with each item, a priceassociated with each item, etc.). In various embodiments, therelationship determination module 250 also may store a hierarchy ofcategories in the content store 210. In some embodiments, therelationship determination module 250 may determine a set of metadataassociated with each item based on the information describing the item.In such embodiments, the set of metadata associated with each item maycorrespond to a set of categories associated with the item. Furthermore,in such embodiments, the relationship determination module 250 may thengenerate the hierarchy of categories based on the set of metadataassociated with each item. For example, suppose that informationdescribing an item associated with an entity having a presence on theonline system 140 indicates that a color of the item is “midnight.” Inthis example, based on reviews of the item mentioning the color “black”and a set of pixel values associated with one or more images of theitem, a set of metadata associated with the item determined by therelationship determination module 250 may indicate that the color of theitem is black. In this example, once the relationship determinationmodule 250 has determined a set of metadata associated with eachadditional item associated with the entity based on informationdescribing the corresponding item, the relationship determination module250 may then generate a hierarchy of categories based on the set ofmetadata associated with each item.

In embodiments in which the relationship determination module 250generates a hierarchy of categories associated with one or more itemsassociated with an entity having a presence on the online system 140,the relationship determination module 250 may determine a measure ofsimilarity between items associated with the entity based on distancesbetween nodes corresponding to categories associated with the items. Todo so, the relationship determination module 250 may identify a nodecorresponding to a category of the hierarchy of categories associatedwith each item based on information describing the item and determine ameasure of similarity between the items based on a distance between thenodes. For example, to determine a measure of similarity between twoitems associated with an entity having a presence on the online system140, the relationship determination module 250 accesses a hierarchy ofcategories associated with items that are associated with the entity. Inthis example, the relationship determination module 250 then identifiesa node corresponding to a category associated with each item based on aset of information describing the corresponding item and determines adistance between the nodes (e.g., a minimum number of nodes and/or edgesconnecting the nodes). Continuing with this example, the relationshipdetermination module 250 may then determine a measure of similaritybetween the items based on the distance, such that the measure ofsimilarity between the items is inversely proportional to the distance.

In some embodiments, the relationship determination module 250 mayidentify a set of items associated with an entity having a presence onthe online system 140 that have at least a threshold measure ofsimilarity to an additional item associated with the entity based on ameasure of similarity between each item associated with the entity andthe additional item. In embodiments in which the relationshipdetermination module 250 generates embeddings in an embedding spaceassociated with the entity, the relationship determination module 250may do so by identifying an embedding corresponding to the additionalitem, as well as a set of embeddings that are within a thresholddistance of this embedding. In such embodiments, the relationshipdetermination module 250 may identify the set of items that have atleast a threshold measure of similarity to the additional item byidentifying the set of items corresponding to the set of embeddings. Inembodiments in which the relationship determination module 250 generatesa hierarchy of categories associated with one or more items associatedwith the entity, the relationship determination module 250 may identifya set of items associated with the entity having at least a thresholdmeasure of similarity to an additional item associated with the entityby identifying a node corresponding to a category associated with theadditional item, as well as a set of nodes that are within a thresholddistance of this node. In such embodiments, the relationshipdetermination module 250 may identify the set of items that have atleast a threshold measure of similarity to the additional item byidentifying the set of items associated with categories corresponding tothe set of nodes. The functionality of the relationship determinationmodule 250 is further described below in conjunction with FIG. 3.

The web server 255 links the online system 140 via the network 120 tothe one or more client devices 110, as well as to the one or morethird-party systems 130. The web server 255 serves web pages, as well asother content, such as JAVA®, FLASH®, XML and so forth. The web server255 may receive and route messages between the online system 140 and theclient device 110, for example, instant messages, queued messages (e.g.,email), text messages, short message service (SMS) messages, or messagessent using any other suitable messaging technique. A user may send arequest to the web server 255 to upload information (e.g., images orvideos) that are stored in the content store 210. Additionally, the webserver 255 may provide application programming interface (API)functionality to send data directly to native client device operatingsystems, such as IOS®, ANDROID™, WEBOS® or BlackberryOS.

Arranging Information Describing Items Within a Page Maintained in anOnline System Based on an Interaction with a Link to the Page

FIG. 3 is a flow chart of a method for arranging information describingitems within a page maintained in an online system based on aninteraction with a link to the page. In other embodiments, the methodmay include different and/or additional steps than those shown in FIG.3. Additionally, steps of the method may be performed in a differentorder than the order described in conjunction with FIG. 3.

The online system 140 receives 305 information describing one or moreitems associated with an entity having a presence on the online system140, in which the information includes one or more images of each item.In some embodiments, the information may be received 305 from the entity(e.g., in a catalog of products). The item(s) may include one or moreproducts, such as clothing items, shoes, electronics, cars, furniture,etc., one or more digital items, such as music, movies, games,applications, etc., or any other suitable types of items. The entity maybe a merchant of the item(s), a manufacturer/creator of the item(s), adistributor of the item(s), or any other suitable type of entity thatmay be associated with the item(s). In addition to one or more images ofeach item, the information describing the item(s) may include a brand oran artist associated with the item(s), a type of the item(s), a style ora genre associated with the item(s), a version or a model associatedwith the item(s), a color associated with the item(s), a material or aset of specifications associated with the item(s), a price associatedwith the item(s), or any other suitable types of information that may beassociated with the item(s). For example, if an item corresponds to aspecific laptop, information describing the laptop received 305 by theonline system 140 may include one or more images of the laptop, a brandof the laptop, information indicating that the item is a type ofcomputer, information indicating that the style of the computercorresponds to a laptop, information describing a color of the laptop,information describing a model of the laptop, a set of specificationsassociated with the laptop (e.g., screen size, memory, storage, etc.),and a retail price of the laptop.

The online system 140 then receives 310 a content item to be presentedto one or more viewing users of the online system 140, in which thecontent item includes an image. The online system 140 may receive 310the content item from the entity or from a content-providing user of theonline system 140 other than the entity. For example, the online system140 may receive 310 the content item from the entity, in which thecontent item includes a caption and an image. In some embodiments, thecontent item also or alternatively may include additional data, such asvideo data, audio data, text data (e.g., in one or more tags), one ormore additional images, etc. In embodiments in which the content itemincludes video data, the image may be included in the video data. Forexample, the image may be included in one or more frames of a videoincluded in the content item received 310 from a content-providing userof the online system 140.

In some embodiments, the online system 140 may receive an interactionwith the content item from a viewing user of the online system 140 towhom the content item was presented. Examples of types of interactionsinclude clicking on the content item, expressing a preference for thecontent item, commenting on the content item, saving the content item,sharing the content item with additional users of the online system 140,unsubscribing to content created by an online system user who createdthe content item, reporting the content item as inappropriate, etc. Forexample, the online system 140 may receive a comment on the content itemfrom a viewing user of the online system 140, in which the commentincludes text content and emojis describing the viewing user'senthusiasm for the content item.

The online system 140 then accesses 315 (e.g., using the predictionmodule 235) a trained machine-learning model and applies 320 (e.g.,using the prediction module 235) the machine-learning model to a set ofattributes of the content item to predict a probability that the contentitem includes an image of an item associated with the entity(hereinafter referred to as the “subject item”). In some embodiments,the machine-learning model may be trained by the online system 140(e.g., using the machine-learning module 230), while in otherembodiments, the machine-learning model may be trained by a third-partysystem 130. The machine-learning model may be trained based on a set oftraining data that includes the information received 305 by the onlinesystem 140 describing the item(s) associated with the entity.

To apply 320 the machine-learning model to the set of attributes of thecontent item to predict the probability that the content item includesan image of the subject item, the online system 140 provides an input tothe machine-learning model that includes the set of attributes of thecontent item. Examples of attributes of the content item that may beincluded in the input to the machine-learning model include a set ofpixel values associated with an image included in the content item, atag included in the content item, a caption included in the contentitem, a location associated with the content item, a comment on thecontent item, an audience associated with the content item, an inferredrelationship between the content item and a topic, etc. Based on the setof attributes, the machine-learning model predicts the probability thatthe content item includes an image of the subject item. The onlinesystem 140 then receives an output from the machine-learning modelcorresponding to the predicted probability. In some embodiments, theonline system 140 may access and apply multiple machine-learning modelsthat collectively predict the probability that the content item includesan image of the subject item in an analogous manner.

The online system 140 then determines 325 (e.g., using the predictionmodule 235) whether the probability predicted by the machine-learningmodel is at least a threshold probability. In some embodiments, theonline system 140 may do so by comparing the predicted probability tothe threshold probability. For example, suppose that an output receivedby the online system 140 from the machine-learning model corresponds toan 85.1% probability that the content item includes an image of thesubject item. In this example, if the threshold probability correspondsto an 85% probability, the online system 140 determines 325 that thepredicted probability is at least the threshold probability since 85.1%is equal to or greater than 85%. Alternatively, in the above example, ifthe output received by the online system 140 from the machine-learningmodel corresponds to an 84% probability, the online system 140determines 325 that the predicted probability is less than the thresholdprobability since 84% is not equal to or greater than 85%. In someembodiments, if the online system 140 determines 325 that theprobability predicted by the machine-learning model is not at least thethreshold probability, the online system 140 may repeat some of thesteps described above (e.g., by proceeding back to step 310).

Responsive to determining 325 that the probability predicted by themachine-learning model is at least the threshold probability, the onlinesystem 140 adds 330 (e.g., using the user interface generator 240) alink to the content item. In some embodiments, the online system 140alternatively may add 330 a button that acts as the link. The link added330 to the content item corresponds to a page maintained in the onlinesystem 140. Furthermore, the page is associated with the subject itemand includes a set of the information received 305 by the online system140 describing the item(s) associated with the entity. As describedabove, information describing an item associated with the entity mayinclude one or more images of the item, a brand or an artist associatedwith the item, a type of the item, a style or a genre associated withthe item, a version or a model associated with the item, a colorassociated with the item, a material or a set of specificationsassociated with the item, a price associated with the item, etc.

In some embodiments, the page corresponding to the link added 330 to thecontent item also may include one or more interactive elements (e.g.,buttons, scroll bars, etc.) that allow a viewing user of the onlinesystem 140 to view information describing the item(s) associated withthe entity within the page and/or to perform various actions associatedwith the item(s). For example, if the subject item corresponds to at-shirt, information describing the t-shirt included in the page mayinclude a brand of the t-shirt, a name or a model of the t-shirt, aprice of the t-shirt, etc. Continuing with this example, a viewing userof the online system 140 presented with the page may view additionalinformation (e.g., materials, care instructions, reviews, etc.) aboutthe t-shirt by clicking on a button (e.g., a button labeled “moredetails”) associated with the t-shirt and may add the t-shirt to a cartby clicking on an additional button (e.g., a button labeled “add tocart”) associated with the t-shirt. In the above example, the viewinguser also may interact with a scroll bar to view information describingadditional items (e.g., additional t-shirts) associated with the entitywithin the page.

The online system 140 then determines 335 an opportunity to presentcontent to a viewing user of the online system 140. For example, theonline system 140 may determine 335 the opportunity to present contentto the viewing user upon receiving a request from the viewing user toview a feed of content items associated with a user profile of theviewing user. As an additional example, the online system 140 maydetermine 335 the opportunity to present content to the viewing userupon receiving a request from the viewing user to refresh a feed ofcontent items associated with a user profile of the viewing user.

The online system 140 then sends 340 the content item including the linkfor display to the viewing user. In some embodiments, the content itemmay be included among one or more content items selected (e.g., usingthe content selection module 245) for display to the viewing user. Insuch embodiments, once the online system 140 has selected the contentitem(s), the online system 140 sends 340 the selected content item(s)for display to the viewing user. For example, the online system 140 mayinclude the selected content item(s) in a feed of content items that issent 340 for display in a display area of a client device 110 associatedwith the viewing user.

Responsive to receiving an interaction with the link (or a button thatacts as the link) from the viewing user, the online system 140determines 345 (e.g., using the relationship determination module 250) ameasure of similarity between the subject item and each additional itemassociated with the entity for which the online system 140 received 305information. The online system 140 may determine 345 the measure ofsimilarity based on the information received 305 by the online system140. For example, suppose that the subject item corresponds toheadphones and that information describing the subject item indicatesthat the headphones are a type of accessory for electronic devices. Inthis example, suppose also that additional items associated with theentity include a pair of shorts and an external hard drive and thatinformation describing the additional items indicate that the pair ofshorts is a clothing item while the external hard drive also is a typeof accessory for electronic devices. In the above example, based on theinformation describing each item associated with the entity, the onlinesystem 140 may determine 345 that there is a greater measure ofsimilarity between the headphones and the external hard drive thanbetween the headphones and the pair of shorts.

In some embodiments, the online system 140 may determine 345 the measureof similarity between the subject item and each additional itemassociated with the entity based on distances between embeddings in anembedding space associated with the entity, in which each embeddingcorresponds to an item associated with the entity. In such embodiments,the online system 140 may generate (e.g., using the relationshipdetermination module 250) an embedding corresponding to an itemassociated with the entity based on information describing the itemreceived 305 by the online system 140. In some embodiments, the onlinesystem 140 also may store each embedding corresponding to an item thatis associated with the entity (e.g., in the content store 210). Theonline system 140 may then determine 345 the measure of similaritybetween the subject item and each of the additional items for which theonline system 140 received 305 information by identifying an embeddingcorresponding to the subject item, as well as an additional embeddingcorresponding to each additional item. The online system 140 thendetermines a distance between the embedding and the additional embeddingand determines 345 the measure of similarity between the subject itemand the additional item based on the distance.

FIG. 4 illustrates an example of how the online system 140 may determine345 the measure of similarity between the subject item and eachadditional item associated with the entity based on distances betweenembeddings 400 in an embedding space 405 associated with the entity. Asshown in FIG. 4, for each item for which the online system 140 received305 information, the online system 140 generates an embedding 400 in theembedding space 405 based on information describing the correspondingitem received 305 by the online system 140. In this example, if thesubject item corresponds to a blazer, to determine 345 a measure ofsimilarity between the blazer and an additional item associated with theentity corresponding to a peacoat, the online system 140 identifies anembedding 400A corresponding to the blazer as well as an additionalembedding 400B corresponding to the peacoat. Continuing with thisexample, the online system 140 then determines a distance between theembeddings 400A-B within the embedding space 405 and determines 345 ameasure of similarity between the blazer and the peacoat based on thedistance between the embeddings 400A-B, such that the measure ofsimilarity between the items is inversely proportional to the distancebetween the embeddings 400A-B. In this example, the online system 140may then repeat this process for each additional item associated withthe entity. In the above example, since the measure of similaritybetween two items is inversely proportional to the distance betweenembeddings 400 corresponding to the items, the blazer has a greatermeasure of similarity to the peacoat than it does to a motorcycle jacketsince the distance between the embedding 400A corresponding to theblazer and the embedding 400B corresponding to the peacoat is shorterthan the distance between the embedding 400A corresponding to the blazerand an embedding 400C corresponding to the motorcycle jacket.

In some embodiments, the online system 140 also may determine 345 themeasure of similarity between the subject item and each additional itemassociated with the entity based on distances between nodes in ahierarchy of categories associated with the items, in which each nodecorresponds to a category that may be associated with one or more itemsassociated with the entity. As described above, the hierarchy ofcategories includes multiple nodes representing the categories and edgesconnecting the nodes represent relationships between the categories, inwhich items associated with categories corresponding to nodes that arecloser together are more closely related than items associated withcategories corresponding to nodes that are further apart. Furthermore,different levels of the hierarchy of categories represent differentlevels of specificity, such that categories represented by nodes at alowest level of the hierarchy correspond to a most specific level ofspecificity and categories represented by nodes at a highest level ofthe hierarchy correspond to a most general level of specificity. Theonline system 140 may generate (e.g., using the relationshipdetermination module 250) the hierarchy of categories based on theinformation describing the item(s) received 305 by the online system140. In some embodiments, the online system 140 may determine (e.g.,using the relationship determination module 250) a set of metadataassociated with each item based on the information describing the item,in which the set of metadata corresponds to a set of categoriesassociated with the item. In such embodiments, the online system 140 maythen generate the hierarchy of categories based on the metadataassociated with each item. In various embodiments, the online system 140also may store the hierarchy of categories (e.g., in the content store210).

FIG. 5 Illustrates an example of a hierarchy of categories that theonline system 140 may use to determine 345 the measure of similaritybetween the subject item and each additional item associated with theentity. As shown in FIG. 5, the hierarchy 505 of categories 510 includesmultiple nodes 505, in which each node 505 represents a category 510(i.e., a brand 510A, a type 510B, a style 510C, a color 510D, a material510E, and/or a price 510F) and each edge connecting a pair of nodes 505represents a relationship between categories 510 represented by the pairof nodes 505. In this example, if the subject item corresponds to a$56.99 pair of white leather sandals by Brand A, to determine 345 ameasure of similarity between these sandals and a $59.99 pair of whiteleather sandals by Brand A, the online system 140 identifies a node 505Acorresponding to a category 510 associated with the $56.99 pair of whiteleather sandals by Brand A as well as an additional node 505Bcorresponding to a category 510 associated with the $59.99 pair of whiteleather sandals by Brand A. Continuing with this example, the onlinesystem 140 then determines a distance between the nodes 505A-B (e.g., aminimum number of nodes 505 and/or edges connecting the nodes 505A-B)and determines 345 a measure of similarity between the sandals based onthe distance, such that the measure of similarity between the sandals isinversely proportional to the distance. In the above example, since themeasure of similarity between two items is inversely proportional to thedistance between nodes 505 corresponding to categories 510 associatedwith the items, the $56.99 pair of white leather sandals by Brand A hasa greater measure of similarity to the $59.99 pair of white leathersandals by Brand A than it does to a $99.99 pair of black suede boots byBrand A since the distance between the nodes 505A-B corresponding to thecategories 510 associated with the sandals is shorter than the distancebetween the node 505A corresponding to the category 510 associated withthe $56.99 pair of white leather sandals by Brand A and a node 505Ccorresponding to a category 510 associated with the boots.

In some embodiments, responsive to receiving an interaction with thelink (or a button that acts as the link) from the viewing user, theonline system 140 may identify (e.g., using the relationshipdetermination module 250) a set of the item(s) for which the onlinesystem 140 received 305 information that have at least a thresholdmeasure of similarity to the subject item. The online system 140 mayidentify the set of items based on a measure of similarity between thesubject item and each additional item associated with the entity. Inembodiments in which the online system 140 generates embeddings in anembedding space associated with the entity, the online system 140 mayidentify an embedding corresponding to the subject item, as well as aset of embeddings that are within a threshold distance of thisembedding. In such embodiments, the online system 140 may identify theset of items that have at least the threshold measure of similarity tothe subject item by identifying the set of items corresponding to theset of embeddings. In embodiments in which the online system 140generates a hierarchy of categories associated with one or more itemsassociated with the entity, the online system 140 may identify a nodecorresponding to a category associated with the subject item, as well asa set of nodes that are within a threshold distance of this node (e.g.,such that each of the set of nodes is within a threshold number of nodesand/or edges from the node corresponding to the category associated withthe subject item). The online system 140 then identifies the set ofitems that have at least the threshold measure of similarity to thesubject item by identifying the set of items associated with categoriescorresponding to the set of nodes.

Referring back to FIG. 3, once the online system 140 has determined 345the measure of similarity between the subject item and each additionalitem for which the online system 140 received 305 information, theonline system 140 arranges 350 (e.g., using the user interface generator240) a set of the information received 305 by the online system 140within the page associated with the subject item. The online system 140arranges 350 the information based on the measure of similarity betweenthe subject item and each additional item associated with the entity. Insome embodiments, items that are more similar to the subject item are ina more prominent position within an area of the page than items that arenot as similar to the subject item.

As shown in the example of FIG. 6, suppose that the subject itemcorresponds to a t-shirt 620A with a small heart design and that aftersending 340 the content item 600 including an image of the t-shirt 620Afor display in a display area of a client device 110 associated with theviewing user, the online system 140 has received an interaction with abutton 605A that acts as the link. In this example, suppose also thatthe online system 140 has determined 345 the measure of similaritybetween the t-shirt 620A and each additional item associated with theentity. Continuing with this example, below an area 610 of the page thatincludes information describing the t-shirt 620A, the online system 140may arrange 350 information describing additional items 620B-Dassociated with the entity within an additional area 615 of the pagebased on a measure of similarity between each additional item 620B-D andthe t-shirt 620A (e.g., by ranking the additional items 620B-D based onhow similar they are to the t-shirt 620A). In this example, informationdescribing a t-shirt 620B with a large heart design is arranged 350 in amore prominent position of the area 615 of the page than informationdescribing other items associated with the entity that are not assimilar to the t-shirt 620A with the small heart design since both thet-shirt 620A with the small heart design and the t-shirt 620B with thelarge heart design have similar designs. Similarly, in this example,information describing a t-shirt 620C with a small star design is in amore prominent position within the area 615 of the page than informationdescribing a t-shirt 620D with a large star design since both thet-shirt 620A with the small heart design and the t-shirt 620C with thesmall star design have similar sized designs. As described above, insome embodiments, the page associated with the subject item may includeone or more interactive elements. As shown in FIG. 6, the page mayinclude a button 605B with which the viewing user may interact to viewinformation describing additional items (e.g., additional t-shirts)associated with the entity within the page.

In some embodiments, rather than including all of the informationdescribing the item(s) for which the online system 140 received 305information in the page associated with the subject item, the onlinesystem 140 also or alternatively may include (e.g., using the userinterface generator 240) only the information that describes a set ofthe item(s) that have at least a threshold measure of similarity to thesubject item. For example, referring back to FIG. 6, suppose that theonline system 140 received 305 information describing six itemsassociated with the entity, but the three t-shirts 620B-D are the onlyitems associated with the entity that have at least a threshold measureof similarity to the t-shirt 620A associated with the page maintained inthe online system 140. In this example, the online system 140 mayinclude only the information describing the three t-shirts 620B-D withinthe page. In the above example, the online system 140 also may arrange350 the information describing the three t-shirts 620B-D based on themeasure of similarity between the t-shirt 620A corresponding to thesubject item and each of the three t-shirts 620B-D associated with theentity, as described above.

Referring once more to FIG. 3, the online system 140 then sends 355 thepage for display to the viewing user. For example, the online system 140sends 355 the page for display in a display area of a client device 110associated with the viewing user. As shown in the example of FIG. 6, thepage may include an area 610 with information describing the subjectitem (i.e., the t-shirt 620A), as well as an additional area 615 withinformation describing additional items (e.g., t-shirts 620B-D) that arearranged 350 based on their measure of similarity to the subject item.

SUMMARY

The foregoing description of the embodiments has been presented for thepurpose of illustration; it is not intended to be exhaustive or to limitthe patent rights to the precise forms disclosed. Persons skilled in therelevant art can appreciate that many modifications and variations arepossible in light of the above disclosure.

Some portions of this description describe the embodiments in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are commonly used bythose skilled in the data processing arts to convey the substance oftheir work effectively to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. Furthermore, it has alsoproven convenient at times, to refer to these arrangements of operationsas modules, without loss of generality. The described operations andtheir associated modules may be embodied in software, firmware,hardware, or any combinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments also may relate to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, and/or it may comprise a general-purpose computingdevice selectively activated or reconfigured by a computer programstored in the computer. Such a computer program may be stored in anon-transitory, tangible computer readable storage medium, or any typeof media suitable for storing electronic instructions, which may becoupled to a computer system bus. Furthermore, any computing systemsreferred to in the specification may include a single processor or maybe architectures employing multiple processor designs for increasedcomputing capability.

Embodiments also may relate to a product that is produced by a computingprocess described herein. Such a product may comprise informationresulting from a computing process, where the information is stored on anon-transitory, tangible computer readable storage medium and mayinclude any embodiment of a computer program product or other datacombination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the patent rights be limitednot by this detailed description, but rather by any claims that issue onan application based hereon. Accordingly, the disclosure of theembodiments is intended to be illustrative, but not limiting, of thescope of the patent rights, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: receiving informationdescribing one or more items associated with an entity having a presenceon an online system, the information comprising one or more images ofeach of the one or more items; receiving a content item to be presentedto one or more viewing users of the online system, the content itemcomprising an image; accessing a trained machine-learning model, thetrained machine-learning model trained based at least in part on theinformation describing the one or more items; for at least one targetitem of the plurality of items associated with the entity, applying thetrained machine-learning model to a set of attributes of the contentitem to predict a probability that the image of the content itemcontains the target item; determining that the image contains the targetitem based on the predicted probability; adding, to the content item, alink to a page associated with the target item; and sending the contentitem comprising the link for display to the one or more viewing users ofthe online system.
 2. The method of claim 1, wherein the informationdescribing the one or more items comprises one or more of: a colorassociated with an item of the one or more items, a style associatedwith an item of the one or more items, a genre associated with an itemof the one or more items, a material associated with an item of the oneor more items, a type associated with an item of the one or more items,a brand associated with an item of the one or more items, an artistassociated with an item of the one or more items, a version associatedwith an item of the one or more items, a model associated with an itemof the one or more items, a set of specifications associated with anitem of the one or more items, and a price associated with an item ofthe one or more items.
 3. The method of claim 1, further comprising:receiving an interaction with the link from a viewing user of the one ormore viewing users; responsive to receiving the interaction with thelink, determining a measure of similarity between the item and eachadditional item of the one or more items, the measure of similaritydetermined based at least in part on the information describing the oneor more items; and rearranging the set of the information describing theone or more items within the page based at least in part on the measureof similarity between the item and each additional item of the one ormore items.
 4. The method of claim 3, wherein determining the measure ofsimilarity between the item and each additional item of the one or moreitems comprises: for each of the one or more items, generating anembedding in an embedding space based at least in part on a set of theinformation describing a corresponding item; identifying an embeddingcorresponding to the item; and for each additional item of the one ormore items: identifying an additional embedding corresponding to theadditional item, determining a distance between the embeddingcorresponding to the item and the additional embedding corresponding tothe additional item, and determining the measure of similarity betweenthe item and the additional item based at least in part on the distance.5. The method of claim 3, further comprising: responsive to receivingthe interaction with the link, identifying a set of the one or moreitems having at least a threshold measure of similarity to the itembased at least in part on the measure of similarity between the item andeach additional item of the one or more items; and including informationdescribing the item and the set of the one or more items having at leastthe threshold measure of similarity to the item in the page.
 6. Themethod of claim 5, wherein identifying the set of the one or more itemshaving at least the threshold measure of similarity to the itemcomprises: for each of the one or more items, generating an embedding inan embedding space based at least in part on a set of the informationdescribing a corresponding item; identifying an embedding correspondingto the item; identifying a set of embeddings within a threshold distanceof the embedding corresponding to the item; and identifying the set ofthe one or more items having at least the threshold measure ofsimilarity to the item, wherein the set of the one or more itemscorrespond to the set of embeddings.
 7. The method of claim 5, whereinidentifying the set of the one or more items having at least thethreshold measure of similarity to the item comprises: determining a setof metadata associated with each of the one or more items based at leastin part on the information describing the one or more items; generatinga hierarchy of categories associated with the one or more items based atleast in part on the set of metadata associated with each of the one ormore items, the hierarchy of categories comprising a plurality of nodesand one or more edges, wherein each of the plurality of nodes representsa category and each of the one or more edges connecting a pair of theplurality of nodes represents a relationship between a plurality ofcategories represented by the pair of nodes; identifying a nodecorresponding to a category of the hierarchy of categories associatedwith the item based at least in part on a set of the informationdescribing the item; for each additional item of the one or more items,identifying an additional node corresponding to an additional categoryof the hierarchy of categories associated with the additional item basedat least in part on a set of the information describing a correspondingitem; identifying a set of nodes within a threshold distance of the nodecorresponding to the category of the hierarchy of categories associatedwith the item; and identifying the set of the one or more items havingat least the threshold measure of similarity to the item, wherein theset of the one or more items correspond to the set of nodes.
 8. Themethod of claim 1, wherein the set of attributes of the content itemcomprises one or more selected from the group consisting of: a set ofpixel values associated with the image comprising the content item, atag comprising the content item, a link comprising the content item, acaption comprising the content item, a location associated with thecontent item, a comment on the content item, an audience associated withthe content item, an inferred relationship between the content item anda topic, and any combination thereof.
 9. The method of claim 1, whereinthe content item is received from the entity.
 10. The method of claim 1,wherein the content item is received from a content-providing user ofthe online system other than the entity.
 11. A computer program productcomprising a non-transitory computer readable storage medium havinginstructions encoded thereon that, when executed by a processor, causethe processor to: receive information describing one or more itemsassociated with an entity having a presence on an online system, theinformation comprising one or more images of each of the one or moreitems; receive a content item to be presented to one or more viewingusers of the online system, the content item comprising an image; accessa trained machine-learning model, the trained machine-learning modeltrained based at least in part on the information describing the one ormore items; for at least one target item of the plurality of itemsassociated with the entity, apply the trained machine-learning model toa set of attributes of the content item to predict a probability thatthe image of the content item contains the target item; determine thatthe image contains the target item based on the predicted probability;add, to the content item, a link to a page associated with the targetitem; and send the content item comprising the link for display to theone or more viewing users of the online system.
 12. The computer programproduct of claim 11, wherein the information describing the one or moreitems comprises one or more of: a color associated with an item of theone or more items, a style associated with an item of the one or moreitems, a genre associated with an item of the one or more items, amaterial associated with an item of the one or more items, a typeassociated with an item of the one or more items, a brand associatedwith an item of the one or more items, an artist associated with an itemof the one or more items, a version associated with an item of the oneor more items, a model associated with an item of the one or more items,a set of specifications associated with an item of the one or moreitems, and a price associated with an item of the one or more items. 13.The computer program product of claim 11, wherein the non-transitorycomputer-readable storage medium further has instructions encodedthereon that, when executed by the processor, cause the processor to:receive an interaction with the link from a viewing user of the one ormore viewing users; responsive to receiving the interaction with thelink, determine a measure of similarity between the item and eachadditional item of the one or more items, the measure of similaritydetermined based at least in part on the information describing the oneor more items; and rearrange the set of the information describing theone or more items within the page based at least in part on the measureof similarity between the item and each additional item of the one ormore items.
 14. The computer program product of claim 13, whereindetermine the measure of similarity between the item and each additionalitem of the one or more items comprises: for each of the one or moreitems, generate an embedding in an embedding space based at least inpart on a set of the information describing a corresponding item;identify an embedding corresponding to the item; and for each additionalitem of the one or more items: identify an additional embeddingcorresponding to the additional item, determine a distance between theembedding corresponding to the item and the additional embeddingcorresponding to the additional item, and determine the measure ofsimilarity between the item and the additional item based at least inpart on the distance.
 15. The computer program product of claim 13,wherein the non-transitory computer-readable storage medium further hasinstructions encoded thereon that, when executed by the processor, causethe processor to: responsive to receiving the interaction with the link,identify a set of the one or more items having at least a thresholdmeasure of similarity to the item based at least in part on the measureof similarity between the item and each additional item of the one ormore items; and including information describe the item and the set ofthe one or more items having at least the threshold measure ofsimilarity to the item in the page.
 16. The computer program product ofclaim 15, wherein identify the set of the one or more items having atleast the threshold measure of similarity to the item comprises: foreach of the one or more items, generate an embedding in an embeddingspace based at least in part on a set of the information describing acorresponding item; identify an embedding corresponding to the item;identify a set of embeddings within a threshold distance of theembedding corresponding to the item; and identify the set of the one ormore items having at least the threshold measure of similarity to theitem, wherein the set of the one or more items correspond to the set ofembeddings.
 17. The computer program product of claim 15, whereinidentify the set of the one or more items having at least the thresholdmeasure of similarity to the item comprises: determine a set of metadataassociated with each of the one or more items based at least in part onthe information describing the one or more items; generate a hierarchyof categories associated with the one or more items based at least inpart on the set of metadata associated with each of the one or moreitems, the hierarchy of categories comprising a plurality of nodes andone or more edges, wherein each of the plurality of nodes represents acategory and each of the one or more edges connecting a pair of theplurality of nodes represents a relationship between a plurality ofcategories represented by the pair of nodes; identify a nodecorresponding to a category of the hierarchy of categories associatedwith the item based at least in part on a set of the informationdescribing the item; for each additional item of the one or more items,identify an additional node corresponding to an additional category ofthe hierarchy of categories associated with the additional item based atleast in part on a set of the information describing a correspondingitem; identify a set of nodes within a threshold distance of the nodecorresponding to the category of the hierarchy of categories associatedwith the item; and identify the set of the one or more items having atleast the threshold measure of similarity to the item, wherein the setof the one or more items correspond to the set of nodes.
 18. Thecomputer program product of claim 11, wherein the set of attributes ofthe content item comprises one or more selected from the groupconsisting of: a set of pixel values associated with the imagecomprising the content item, a tag comprising the content item, a linkcomprising the content item, a caption comprising the content item, alocation associated with the content item, a comment on the contentitem, an audience associated with the content item, an inferredrelationship between the content item and a topic, and any combinationthereof.
 19. The computer program product of claim 11, wherein thecontent item is received from the entity.
 20. The computer programproduct of claim 11, wherein the content item is received from acontent-providing user of the online system other than the entity.